OpenRaiser/Intern-Atlas huggingface dataset:
On the rate distortion function of Bernoulli Gaussian sequences
Venue: arXiv cs.IT 2009
Year: 2009

In this paper, we study the rate distortion function of the i.i.d sequence of multiplications of a Bernoulli $p$ random variable and a gaussian random variable $\sim N(0,1)$. We use a new technique in the derivation of the lower bound in which we establish the duality between channel coding and lossy source coding in the strong sense. We improve the lower bound on the rate distortion function over the best known lower bound by $p\log_2\frac{1}{p}$ if distortion $D$ is small. This has some interesting implications on sparse signals where $p$ is small since the known gap between the lower and upper bound is $H(p)$. This improvement in the lower bound shows that the lower and upper bounds are almost identical for sparse signals with small distortion because $\lim\limits_{p\to 0}\frac{p\log_2\frac{1}{p}}{H(p)}=1$.
iKlax: a New Musical Audio Format for Active Listening
Venue: arXiv cs.SD 2009
Year: 2009

In this paper, we are presenting a new model for interactive music. Unlike most interactive systems, our model is based on file organization, but does not require digital audio treatments. This model includes a definition of a constraints system and its solver. The products of this project are intended for the general public, inexperienced users, as well as professional musicians, and will be distributed commercially. We are here presenting three products of this project. The difficulty of this project is to design a technology and software products for interactive music which must be easy to use by the general public and by professional composers.
On the Sum Capacity of A Class of Cyclically Symmetric Deterministic Interference Channels
Venue: arXiv cs.IT 2009
Year: 2009

Certain deterministic interference channels have been shown to accurately model Gaussian interference channels in the asymptotic low-noise regime. Motivated by this correspondence, we investigate a K user-pair, cyclically symmetric, deterministic interference channel in which each receiver experiences interference only from its neighboring transmitters (Wyner model). We establish the sum capacity for a large set of channel parameters, thus generalizing previous results for the 2-pair case.
A Quantum Key Distribution Network Through Single Mode Optical Fiber
Venue: arXiv cs.CR 2009
Year: 2009

Quantum key distribution (QKD) has been developed within the last decade that is provably secure against arbitrary computing power, and even against quantum computer attacks. Now there is a strong need of research to exploit this technology in the existing communication networks. In this paper we have presented various experimental results pertaining to QKD like Raw key rate and Quantum bit error rate (QBER). We found these results over 25 km single mode optical fiber. The experimental setup implemented the enhanced version of BB84 QKD protocol. Based upon the results obtained, we have presented a network design which can be implemented for the realization of large scale QKD networks. Furthermore, several new ideas are presented and discussed to integrate the QKD technique in the classical communication networks.
On The Positive Definiteness of Polarity Coincidence Correlation Coefficient Matrix
Venue: arXiv cs.IT 2009
Year: 2009

Polarity coincidence correlator (PCC), when used to estimate the covariance matrix on an element-by-element basis, may not yield a positive semi-definite (PSD) estimate. Devlin et al. [1], claimed that element-wise PCC is not guaranteed to be PSD in dimensions p>3 for real signals. However, no justification or proof was available on this issue. In this letter, it is proved that for real signals with p<=3 and for complex signals with p<=2, a PSD estimate is guaranteed. Counterexamples are presented for higher dimensions which yield invalid covariance estimates.
A Keygraph Classification Framework for Real-Time Object Detection
Venue: arXiv cs.CV 2009
Year: 2009

In this paper, we propose a new approach for keypoint-based object detection. Traditional keypoint-based methods consist in classifying individual points and using pose estimation to discard misclassifications. Since a single point carries no relational features, such methods inherently restrict the usage of structural information to the pose estimation phase. Therefore, the classifier considers purely appearance-based feature vectors, thus requiring computationally expensive feature extraction or complex probabilistic modelling to achieve satisfactory robustness. In contrast, our approach consists in classifying graphs of keypoints, which incorporates structural information during the classification phase and allows the extraction of simpler feature vectors that are naturally robust. In the present work, 3-vertices graphs have been considered, though the methodology is general and larger order graphs may be adopted. Successful experimental results obtained for real-time object detection in video sequences are reported.
Hierarchical Triple-Modular Redundancy (H-TMR) Network For Digital Systems
Venue: arXiv cs.OH 2009
Year: 2009

Hierarchical application of Triple-Modular Redundancy (TMR) increases fault tolerance of digital Integrated Circuit (IC). In this paper, a simple probabilistic model was proposed for analysis of fault masking performance of hierarchical TMR networks. Performance improvements obtained by second order TMR network were theoretically compared with first order TMR network.
Graphical Reasoning in Compact Closed Categories for Quantum Computation
Venue: arXiv cs.SC 2009
Year: 2009

Compact closed categories provide a foundational formalism for a variety of important domains, including quantum computation. These categories have a natural visualisation as a form of graphs. We present a formalism for equational reasoning about such graphs and develop this into a generic proof system with a fixed logical kernel for equational reasoning about compact closed categories. Automating this reasoning process is motivated by the slow and error prone nature of manual graph manipulation. A salient feature of our system is that it provides a formal and declarative account of derived results that can include `ellipses'-style notation. We illustrate the framework by instantiating it for a graphical language of quantum computation and show how this can be used to perform symbolic computation.
Genetic algorithm based optimization and post optimality analysis of multi-pass face milling
Venue: arXiv cs.CE 2009
Year: 2009

This paper presents an optimization technique for the multi-pass face milling process. Genetic algorithm (GA) is used to obtain the optimum cutting parameters by minimizing the unit production cost for a given amount of material removal. Cutting speed, feed and depth of cut for the finish and rough passes are the cutting parameters. An equal depth of cut for roughing passes has been considered. A lookup table containing the feasible combinations of depth of cut in finish and rough passes is generated so as to reduce the number of variables by one. The resulting mixed integer nonlinear optimization problem is solved in a single step using GA. The entire technique is demonstrated in a case study. Post optimality analysis of the example problem is done to develop a strategy for optimizing without running GA again for different values of total depth of cut.
Finding Exact Minimal Polynomial by Approximations
Venue: arXiv cs.CC 2009
Year: 2009

We present a new algorithm for reconstructing an exact algebraic number from its approximate value using an improved parameterized integer relation construction method. Our result is consistent with the existence of error controlling on obtaining an exact rational number from its approximation. The algorithm is applicable for finding exact minimal polynomial by its approximate root. This also enables us to provide an efficient method of converting the rational approximation representation to the minimal polynomial representation, and devise a simple algorithm to factor multivariate polynomials with rational coefficients. Compared with other methods, this method has the numerical computation advantage of high efficiency. The experimental results show that the method is more efficient than \emph{identify} in \emph{Maple} 11 for obtaining an exact algebraic number from its approximation. In this paper, we completely implement how to obtain exact results by numerical approximate computations.
Matrix Graph Grammars and Monotone Complex Logics
Venue: arXiv cs.DM 2009
Year: 2009

Graph transformation is concerned with the manipulation of graphs by means of rules. Graph grammars have been traditionally studied using techniques from category theory. In previous works, we introduced Matrix Graph Grammars (MGGs) as a purely algebraic approach for the study of graph grammars and graph dynamics, based on the representation of graphs by means of their adjacency matrices. MGGs have been succesfully applied to problems such as applicability of rule sequences, sequentialization and reachability, providing new analysis techniques and generalizing and improving previous results. Our next objective is to generalize MGGs in order to approach computational complexity theory and "static" properties of graphs out of the "dynamics" of certain grammars. In the present work, we start building bridges between MGGs and complexity by introducing what we call "Monotone Complex Logic", which allows establishing a (bijective) link between MGGs and complex analysis. We use this logic to recast the formulation and basic building blocks of MGGs as more proper geometric and analytic concepts (scalar products, norms, distances). MGG rules can also be interpreted - via operators - as complex numbers. Interestingly, the subset they define can be characterized as the Sierpinski gasket.
Compressed Representations of Permutations, and Applications
Venue: arXiv cs.DS 2009
Year: 2009

We explore various techniques to compress a permutation $π$ over n integers, taking advantage of ordered subsequences in $π$, while supporting its application $π$(i) and the application of its inverse $π^{-1}(i)$ in small time. Our compression schemes yield several interesting byproducts, in many cases matching, improving or extending the best existing results on applications such as the encoding of a permutation in order to support iterated applications $π^k(i)$ of it, of integer functions, and of inverted lists and suffix arrays.
A Polynomial Kernel For Multicut In Trees
Venue: arXiv cs.DM 2009
Year: 2009

The MULTICUT IN TREES problem consists in deciding, given a tree, a set of requests (i.e. paths in the tree) and an integer k, whether there exists a set of k edges cutting all the requests. This problem was shown to be FPT by Guo and Niedermeyer. They also provided an exponential kernel. They asked whether this problem has a polynomial kernel. This question was also raised by Fellows. We show that MULTICUT IN TREES has a polynomial kernel.
On Why and What of Randomness
Venue: arXiv cs.OH 2009
Year: 2009

This paper has several objectives. First, it separates randomness from lawlessness and shows why even genuine randomness does not imply lawlessness. Second, it separates the question -why should I call a phenomenon random? (and answers it in part one) from the patent question -What is a random sequence? -for which the answer lies in Kolmogorov complexity (which is explained in part two). While answering the first question the note argues why there should be four motivating factors for calling a phenomenon random: ontic, epistemic, pseudo and telescopic, the first two depicting genuine randomness and the last two false. Third, ontic and epistemic randomness have been distinguished from ontic and epistemic probability. Fourth, it encourages students to be applied statisticians and advises against becoming armchair theorists but this is interestingly achieved by a straight application of telescopic randomness. Overall, it tells (the teacher) not to jump to probability without explaining randomness properly first and similarly advises the students to read (and understand) randomness minutely before taking on probability.
Database Transposition for Constrained (Closed) Pattern Mining
Venue: arXiv cs.LG 2009
Year: 2009

Recently, different works proposed a new way to mine patterns in databases with pathological size. For example, experiments in genome biology usually provide databases with thousands of attributes (genes) but only tens of objects (experiments). In this case, mining the "transposed" database runs through a smaller search space, and the Galois connection allows to infer the closed patterns of the original database. We focus here on constrained pattern mining for those unusual databases and give a theoretical framework for database and constraint transposition. We discuss the properties of constraint transposition and look into classical constraints. We then address the problem of generating the closed patterns of the original database satisfying the constraint, starting from those mined in the "transposed" database. Finally, we show how to generate all the patterns satisfying the constraint from the closed ones.
Fountain Codes Based Distributed Storage Algorithms for Large-scale Wireless Sensor Networks
Venue: arXiv cs.IT 2009
Year: 2009

We consider large-scale sensor networks with n nodes, out of which k are in possession, (e.g., have sensed or collected in some other way) k information packets. In the scenarios in which network nodes are vulnerable because of, for example, limited energy or a hostile environment, it is desirable to disseminate the acquired information throughout the network so that each of the n nodes stores one (possibly coded) packet and the original k source packets can be recovered later in a computationally simple way from any (1 + ε)k nodes for some small ε> 0. We developed two distributed algorithms for solving this problem based on simple random walks and Fountain codes. Unlike all previously developed schemes, our solution is truly distributed, that is, nodes do not know n, k or connectivity in the network, except in their own neighborhoods, and they do not maintain any routing tables. In the first algorithm, all the sensors have the knowledge of n and k. In the second algorithm, each sensor estimates these parameters through the random walk dissemination. We present analysis of the communication/transmission and encoding/decoding complexity of these two algorithms, and provide extensive simulation results as well
On the Additive Constant of the k-server Work Function Algorithm
Venue: arXiv cs.DS 2009
Year: 2009

We consider the Work Function Algorithm for the k-server problem. We show that if the Work Function Algorithm is c-competitive, then it is also strictly (2c)-competitive. As a consequence of [Koutsoupias and Papadimitriou, JACM 1995] this also shows that the Work Function Algorithm is strictly (4k-2)-competitive.
Asymptotically Optimal Lower Bounds on the NIH-Multi-Party Information
Venue: arXiv cs.CC 2009
Year: 2009

Here we prove an asymptotically optimal lower bound on the information complexity of the k-party disjointness function with the unique intersection promise, an important special case of the well known disjointness problem, and the ANDk-function in the number in the hand model. Our (n/k) bound for disjointness improves on an earlier (n/(k log k)) bound by Chakrabarti et al. (2003), who obtained an asymptotically tight lower bound for one-way protocols, but failed to do so for the general case. Our result eliminates both the gap between the upper and the lower bound for unrestricted protocols and the gap between the lower bounds for one-way protocols and unrestricted protocols.
A baby steps/giant steps Monte Carlo algorithm for computing roadmaps in smooth compact real hypersurfaces
Venue: arXiv cs.SC 2009
Year: 2009

We consider the problem of constructing roadmaps of real algebraic sets. The problem was introduced by Canny to answer connectivity questions and solve motion planning problems. Given $s$ polynomial equations with rational coefficients, of degree $D$ in $n$ variables, Canny's algorithm has a Monte Carlo cost of $s^n\log(s) D^{O(n^2)}$ operations in $\mathbb{Q}$; a deterministic version runs in time $s^n \log(s) D^{O(n^4)}$. The next improvement was due to Basu, Pollack and Roy, with an algorithm of deterministic cost $s^{d+1} D^{O(n^2)}$ for the more general problem of computing roadmaps of semi-algebraic sets ($d \le n$ is the dimension of an associated object). We give a Monte Carlo algorithm of complexity $(nD)^{O(n^{1.5})}$ for the problem of computing a roadmap of a compact hypersurface $V$ of degree $D$ in $n$ variables; we also have to assume that $V$ has a finite number of singular points. Even under these extra assumptions, no previous algorithm featured a cost better than $D^{O(n^2)}$.
A competitive comparison of different types of evolutionary algorithms
Venue: arXiv cs.NE 2009
Year: 2009

This paper presents comparison of several stochastic optimization algorithms developed by authors in their previous works for the solution of some problems arising in Civil Engineering. The introduced optimization methods are: the integer augmented simulated annealing (IASA), the real-coded augmented simulated annealing (RASA), the differential evolution (DE) in its original fashion developed by R. Storn and K. Price and simplified real-coded differential genetic algorithm (SADE). Each of these methods was developed for some specific optimization problem; namely the Chebychev trial polynomial problem, the so called type 0 function and two engineering problems - the reinforced concrete beam layout and the periodic unit cell problem respectively. Detailed and extensive numerical tests were performed to examine the stability and efficiency of proposed algorithms. The results of our experiments suggest that the performance and robustness of RASA, IASA and SADE methods are comparable, while the DE algorithm performs slightly worse. This fact together with a small number of internal parameters promotes the SADE method as the most robust for practical use.
Novel anisotropic continuum-discrete damage model capable of representing localized failure of massive structures. Part II: identification from tests under heterogeneous stress field
Venue: arXiv cs.NE 2009
Year: 2009

In Part I of this paper we have presented a simple model capable of describing the localized failure of a massive structure. In this part, we discuss the identification of the model parameters from two kinds of experiments: a uniaxial tensile test and a three-point bending test. The former is used only for illustration of material parameter response dependence, and we focus mostly upon the latter, discussing the inverse optimization problem for which the specimen is subjected to a heterogeneous stress field.
A Unified Approach to Sparse Signal Processing
Venue: arXiv cs.IT 2009
Year: 2009

A unified view of sparse signal processing is presented in tutorial form by bringing together various fields. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common benefits of significant reduction in sampling rate and processing manipulations are revealed. The key applications of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding in finite/real Galois fields is then related to sampling with similar reconstruction algorithms. The methods of Prony, Pisarenko, and MUSIC are next discussed for sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter and Error Locator Polynomials in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method. Such spectral estimation methods is then related to multi-source location and DOA estimation in array processing. The notions of sparse array beamforming and sparse sensor networks are also introduced. Sparsity in unobservable source signals is also shown to facilitate source separation in SCA; the algorithms developed in this area are also widely used in compressed sensing. Finally, the multipath channel estimation problem is shown to have a sparse formulation; algorithms similar to sampling and coding are used to estimate OFDM channels.
Cooperative Spectrum Sensing based on the Limiting Eigenvalue Ratio Distribution in Wishart Matrices
Venue: arXiv cs.IT 2009
Year: 2009

Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. Most of such techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far in this field are based on asymptotical assumptions, due to the difficulties in characterizing the exact distribution of the eigenvalues ratio. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then straightforwardly applied to calculate the decision threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial performance improvement compared to the other eigenvalue-based algorithms.
Modified Papoulis-Gerchberg algorithm for sparse signal recovery
Venue: arXiv cs.IT 2009
Year: 2009

Motivated by the well-known Papoulis-Gerchberg algorithm, an iterative thresholding algorithm for recovery of sparse signals from few observations is proposed. The sequence of iterates turns out to be similar to that of the thresholded Landweber iterations, although not the same. The performance of the proposed algorithm is experimentally evaluated and compared to other state-of-the-art methods.
A Generalization of Nemhauser and Trotter's Local Optimization Theorem
Venue: arXiv cs.CC 2009
Year: 2009

The Nemhauser-Trotter local optimization theorem applies to the NP-hard Vertex Cover problem and has applications in approximation as well as parameterized algorithmics. We present a framework that generalizes Nemhauser and Trotter's result to vertex deletion and graph packing problems, introducing novel algorithmic strategies based on purely combinatorial arguments (not referring to linear programming as the Nemhauser-Trotter result originally did). We exhibit our framework using a generalization of Vertex Cover, called Bounded- Degree Deletion, that has promise to become an important tool in the analysis of gene and other biological networks. For some fixed d \geq 0, Bounded-Degree Deletion asks to delete as few vertices as possible from a graph in order to transform it into a graph with maximum vertex degree at most d. Vertex Cover is the special case of d = 0. Our generalization of the Nemhauser-Trotter theorem implies that Bounded-Degree Deletion has a problem kernel with a linear number of vertices for every constant d. We also outline an application of our extremal combinatorial approach to the problem of packing stars with a bounded number of leaves. Finally, charting the border between (parameterized) tractability and intractability for Bounded-Degree Deletion, we provide a W[2]-hardness result for Bounded-Degree Deletion in case of unbounded d-values.